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machine learning and data harmonization approaches for water quality monitoring and assessment. Responsibilities include developing and validating computational methods to standardize and infer nutrient and
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machine learning, Computer vision, Swarms, Autonomous Robots, hardware security, and Embedded systems development is desired. The successful applicant will work on various projects on robotics and computer
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others and contribute to a team environment. Technical Proficiency: Skilled in using office software, technology, and relevant computer applications. Communication: Strong and clear written and verbal
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Job Description Applications are invited for a full-time Postdoctoral Research Associate position within the Bradley Department of Electrical and Computer Engineering, Wireless@Virginia Tech
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Cognitive Science, Medical Informatics, or a comparable quantitative discipline Proven hands-on experience working with LLMs, foundation models, multimodal AI systems, or related machine-learning technologies
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. Desirable experience in AI-assisted image analysis, machine learning, computational modelling, or building predictive models from biological imaging or cell–material interaction datasets. Strong experience in
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Research Assistant or Postdoctoral Research Associate About the Role The School of Electronic Engineering and Computer Science at Queen Mary University of London seeks to recruit a Research
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sensors, RGB/IR cameras, video systems, insect traps, and other devices to build predictive, AI- and machine-learning based models for monitoring grain quality and detecting deterioration due to mold
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 3 months ago
/Administrative Internal Number: 25848 POSTDOCTORAL ASSOCIATE, Jameel Clinic for Machine Learning in Health, seeks to support a collaborative research program between the laboratories of Professor Omer Yilmaz, Koch
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: Postdoctoral Researcher - Electrical and Computer Engineering Position Type: Faculty Location: Villanova, PA Recruitment Type: Internal/External Applicants Work Schedule: full-time/12-months Department: 204